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Constrained-storage vector quantization with a universal codebook.

S Ramakrishnan1, K Rose, A Gersho

  • 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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This study introduces a novel universal codebook for vector quantization (VQ) to efficiently compress diverse image data. The method optimizes performance and storage, enabling variable encoding rates for improved image compression.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Vector quantization (VQ) requires separate codebooks for diverse data distributions, leading to high memory demands.
  • Existing methods group sources to share codebooks, limiting performance and flexibility.
  • Storage constraints necessitate efficient VQ strategies for multi-source compression.

Purpose of the Study:

  • To develop a storage-efficient VQ framework for multiple, differently distributed sources.
  • To enhance image compression performance under memory limitations.
  • To introduce a flexible VQ approach with variable encoding rates.

Main Methods:

  • Proposed a size-limited universal codebook as a union of overlapping source codebooks.
  • Derived optimality conditions for universal and source codebooks.

Related Experiment Videos

  • Introduced an iterative design algorithm for codebook optimization.
  • Applied the technique to finite-state, multistage, and tree-structured VQ for image coding.

    • Main Results:

    • The universal codebook framework offers greater design flexibility for storage-constrained performance.
    • Variable encoding rates per source are supported, unlike previous clustering methods.
    • Demonstrated effectiveness in image compression applications using various VQ techniques.

    Conclusions:

    • The proposed universal codebook approach provides a flexible and efficient solution for multi-source VQ under storage constraints.
    • This method advances image compression by balancing performance and memory usage.
    • The technique shows promise for applications requiring adaptive and classified quantization.